Smart sensing network

ABSTRACT

Embodiments are described of a sensing network including one or more sensor nodes, wherein each of the one or more sensor nodes includes a gas sensor that can measure the presence, concentration, or both, of one or more airborne pollutants in a nodal coverage area surrounding the sensor node. A sensor base that can measure the presence, concentration, or both, of one or more airborne pollutants is positioned in the nodal coverage area of each of the one or more sensor nodes, wherein the sensor base includes a gas sensor with higher accuracy, higher sensitivity, or both, than the gas sensors of the one or more sensor nodes. One or more servers communicatively coupled to the sensor base and the one or more sensors nodes. The sensor base and the sensor nodes can communicate their measurements to the server and the measurements of the sensor base are used by the server as a reference to correct the measurements of the one or more sensor nodes.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation under 35 U.S.C. § 120 of U.S.application Ser. No. 15/810,409, filed 13 Nov. 2017, which is stillpending and claims priority under 35 U.S.C. § 119(e) to U.S. ProvisionalApp. No. 62/423,033, filed 16 Nov. 2016, which is hereby incorporated byreference in its entirety.

TECHNICAL FIELD

The disclosed embodiments relate generally to air pollution sensing andmonitoring and in particular, but not exclusively, to a smart sensingnetwork for air pollution sensing and monitoring.

BACKGROUND

Air pollution within cities and industrial parks has worsened withmodernization, and public health effects caused by air pollutants (e.g.,chemicals, such as PM2.5, NOx, SOx, O3, and volatile organic compounds(VOCs)) have begun to gain increasing attention. Due to complexpollution sources, it is necessary to obtain denser spatial air qualitymonitoring data to provide the general public with updated air qualityinformation at locations of interest.

Ambient air quality monitoring has been implemented, but becauseexpensive and bulky lab instruments must be used to obtain reliablemonitoring data, only a limited number of monitoring stations can be setup and installed at limited locations. And because the monitoringstations usually require large spaces for instrument setup (especiallywith gas chromatography instruments) they are mostly installed insuburban areas where there is room to accommodate them, with very fewstations installed in cities where there is less room. As a result thereis a need for using small, low-cost sensors to form a network to monitorspatial air quality across the city, which has become popular.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting and non-exhaustive embodiments of the present invention aredescribed with reference to the following figures, wherein likereference numerals refer to like parts throughout the various viewsunless otherwise specified.

FIG. 1 is a block diagram of an embodiment of a smart pollution sensingand monitoring system.

FIGS. 2A-2B are a flowchart and a pair of graphs, respectively,illustrating an embodiment of a process for correcting measurementsreceived from sensor nodes.

FIG. 3 is a block diagram of another embodiment of a smart pollutionsensing and monitoring system.

FIG. 4 is a block diagram of another embodiment of a smart pollutionsensing and monitoring system.

FIG. 5 is a block diagram of another embodiment of a smart pollutionsensing and monitoring system.

FIG. 6 is a block diagram of another embodiment of a smart pollutionsensing and monitoring system.

FIG. 7 is a block diagram of another embodiment of a smart pollutionsensing and monitoring system.

FIG. 8 is a block diagram of another embodiment of a smart pollutionsensing and monitoring system.

DETAILED DESCRIPTION OF THE ILLUSTRATED EMBODIMENTS

Embodiments are described of an apparatus, system and method for a smartsensing network for air pollution sensing and monitoring. Specificdetails are described to provide an understanding of the embodiments,but one skilled in the relevant art will recognize that the inventioncan be practiced without one or more of the described details or withother methods, components, materials, etc. In some instances, well-knownstructures, materials, or operations are not shown or described indetail but are nonetheless encompassed within the scope of theinvention.

Reference throughout this specification to “one embodiment” or “anembodiment” means that a described feature, structure, or characteristiccan be included in at least one described embodiment, so thatappearances of “in one embodiment” or “in an embodiment” do notnecessarily all refer to the same embodiment. Furthermore, theparticular features, structures, or characteristics may be combined inany suitable manner in one or more embodiments.

FIG. 1 illustrates an embodiment of a smart air pollution sensing andmonitoring system 100. System 100 includes a data center 102communicatively coupled to one or more sensor bases B (i.e., B1 and B2in the illustrated embodiment) and one or more sensor nodes S (i.e., S1a-S1 d and S2 a-S2 d in the illustrated embodiment). The illustratedembodiment has two sensor bases B1-B2 and eight sensor nodes S1 aS1 dand S2 a-S2 d, but other embodiments can have more or less sensor basesthan shown and more or less sensor nodes than shown.

Data center 102 includes various hardware elements, such as acommunication interface, one or more servers each including at least onemicroprocessor, memory, and storage, and additional storage space thatcan include one or more databases, for instance to register informationabout the sensor bases B and sensor nodes S including their type,identity, location if fixed or current location if mobile, measurementhistory, etc. The communication interface communicatively couples datacenter 102 to sensor nodes S1 a-S1 d and S2 a-S2 d and sensor bases B1and B2. Sensor nodes S1 a-S1 d and S2 a-S2 d and sensor bases B1-B2 cancommunicate with data center 102 by wire, wirelessly, or by somecombination of wirelessly and wired. For instance, in the illustratedembodiment sensor nodes S1 a and S1 b communicate with data center 102wirelessly via network 104, while sensor nodes S1 c and S1 d communicatewith data center 102 by wire, also via a network 104. Similarly, sensorbase B1 communicates with data center 102 by wire via network 104.Sensor base B2 communicates with data center 102 wirelessly via network104, while sensor nodes S2 a and S2 d communicate with data center 102wirelessly and sensor nodes S2 b and S2 c communicate with data center102 via wire, both via network 104. Network 104 can be the Internet, orcan be a local area network (LAN), a wide area network (WAN), or someother type of network in different embodiments.

Sensor nodes S1 a-S1 d and S2 a-S2 d are divided into sets thatcorrespond to a sensor base: in the illustrated embodiment, sensor nodesS1 a-S1 d are grouped with sensor base B1 and sensor nodes S2 a-S2 d aregrouped with sensor base B2, so that in each grouping there is amany-to-one correspondence between sensor nodes and sensor bases. Otherembodiments need not have this many-to-one correspondence, but caninstead have a one-to-one correspondence between sensor nodes and sensorbases. And although in the illustrated embodiment sensor bases B1-B2 areeach grouped with four sensor nodes, in other embodiments each sensorbase need not be grouped with the same number of sensor nodes.

Each sensor node S has a corresponding coverage area, which is an areain which an air sample gathered for analysis by the sensor node can beconsidered representative. Each sensor base B similarly has acorresponding coverage area. In the illustrated embodiment sensor baseB1 has a coverage area that overlaps with the coverage areas of sensornodes S1 a-S1 d, so that an air sample gathered for analysis by sensorbase B1 can also be considered representative of conditions in thecoverage areas of S1 a-S1 d. Sensor base B2, on the other hand, has acoverage area that does not overlap with the coverage areas of its setof sensor nodes S2 a-S2 d. In an embodiment where B2 is mobile, it canbe moved into and out of the coverage areas of sensor nodes S2 a-S2 d.But in embodiments where B2 is stationary, for sensor base B2 to be ableto collect air samples in the coverage areas of sensors S2 a-S2 d, asampling manifold 106 includes one or more sampling tubes 106 a-106 dfluidly coupled to sensor base B2, with each sampling tube having oneend at sensor base B2 and the other extended into the coverage areas ofsensor nodes S2 a-S2 d. In the context of this application, “fluidlycoupled” means coupled in such a way that fluid can flow one or bothways between two locations.

In some embodiments of system 100, sensor nodes S and sensor bases Boccupy fixed positions and are thus fixed relative to each other. But asdiscussed below, in some embodiments at least one of sensor bases B1 andB2 can be mobile; in these embodiments the positions of sensor bases Brelative to sensor nodes S can change, so that each sensor node need notbe permanently assigned to a particular sensor base. As a result, theexact composition of a group—that is, the numbers and identities ofsensor bases and the numbers and identities of corresponding sensornodes—can change over time. In some embodiments with mobile sensor basesthe changes can be frequent enough that no particular group isrecognizable.

In the illustrated embodiment, sensor nodes S1 a-S1 d and S2 a-S2 d arefixed on-site low-cost air quality monitoring sensors, which aresubstantially less expensive than sensor bases B and can be easilydeployed on-site at various locations to form a high-density sensingnetwork for on-site monitoring and provide big data analysis to delivertime-dense and spatially-dense usable air quality information to thepublic about airborne pollutants. Airborne pollutants can includeairborne chemicals and other airborne contaminants such as, withoutlimitation, organic or inorganic chemicals such as volatile organiccompounds (VOCs), NOx, SOx, O3; airborne particulate matter (PM), suchas PM2.5 (i.e., particulate matter with 2.5 μm diameter) and PM10;compounds; heavy metal contaminants; airborne biological contaminants;etc.

The sensing data from sensor nodes can be wired or wirelessly uploadedto one or more servers for further data correction or analysis. Inanother embodiment, there can be a wired or wireless connection betweensensor nodes S and sensor bases B; for instance, in the illustratedembodiment sensor node S1 b can communicate wirelessly with sensor baseB1, sensor node S1 d can communicate wirelessly with sensor base B2, andsensor node S2 c can communicate by wire with sensor base B2. Inembodiments where sensor nodes S can communicate directly with sensorbases B, instead of transmitting sensor node data directly to datacenter 102, sensor nodes S can transmit sensor node data to data center102 indirectly via a sensor base B. The low-cost sensors can providereal time or more frequent on-site air quality monitoring results. Thelow-cost sensors used for sensing nodes S can suffer from sensitivitydrift, which requires periodic baseline/sensitivity drift check andcorrection.

Sensor bases B1-B2 serve as data references for their correspondingsensor nodes S. Sensor bases B1-B2 include analyzers to collect airsamples and provide accurate, high-quality, and consistent measuredresults with high specificity on individual compound detection. Sensorbases B1-B2 measure air quality at specific locations and times using ahigh-performance air quality analyzer that can collect air samples andanalyze them to provide concentrations of airborne pollutants.Pollutants measured can include, without limitation, organic orinorganic chemicals, such as volatile organic compounds (VOCs), NOx,SOx, O3; airborne particulate matter (PM) such as PM2.5 (i.e.,particulate matter with 2.5 μm diameter) and PM10; compounds; heavymetal contaminants; airborne biological contaminants; etc. Sensor basesB1-B2 can be fixed or mobile; when mobile, sensor bases B can include aposition sensor, such as a GPS receiver, which they can use to reporttheir current location to data center 102. The sensor data formeasurements carried out by sensor bases B1-B2 can be uploaded, by wireor wirelessly, to one or more servers in data center 102. The sensorbase data can then be used as reference for data correction on sensornode results. Each sensor base can also provide periodic reference datafor on-site sensor node data correction, recalibration, or replacement.

FIGS. 2A-2B together illustrate an embodiment of a process 200 by whichmeasurements from the sensor bases B can be used to correct measurementsfrom sensor nodes S. FIG. 2A is a flowchart that will be discussed belowin the context of system 100 and the graphs of FIG. 2B. In system 100the process 200 is carried out primarily by data center 102.

The process starts at block 202. A block 204, data center 102 receivesmeasurements from one or more sensor nodes with which it cancommunicate. At block 206 the process checks whether a current sensorbase measurement is available for that sensor—that is, whether there isa sensor base measurement that is close in both time and space to thereceived sensor node measurements. In an embodiment in which sensor baseB and sensor nodes S are fixed relative to each other, the sensor basemeasurement will essentially always be current. But in embodiments inwhich sensor bases B are not fixed relative to the sensornodes—embodiments in which sensor bases B are mobile, for instance—thesensor base measurement may not be current if the sensor base has notrecently been in the neighborhood of a sensor node from which ameasurement has been received. In one embodiment sensor base B cancollect and analyze air samples at or around the nodal coverage area ofeach sensor node S at a specified fixed frequency, but in otherembodiments sensor base B can collect and analyze air samples at aspecial temporal period. In some embodiments special temporal periodsinclude at random or when needed. In still other embodiments, if thedata center determines that a specific sensor node is starting to driftfaster than expected but still functions acceptably before replacement,the corresponding sensor base (fixed or mobile) that covers the sensornode can be adjusted to increase air sample monitoring frequency aroundthat sensor node to provide more accurate data correction.

If at block 206 there is no current sensor base data available, theprocess proceeds to block 214, where the sensor node data is reported.At block 214 the sensor node data can be reported as is—i.e., withwhatever drift is present in the data. Alternatively, in the absence ofcurrent base sensor data, before the final data is reported the dataserver can utilize advanced artificial intelligence analysis to predicta possible correction based on the previous time-progressingdrift/degradation of the same sensor node.

If in block 206 a current sensor base measurement is available, then theprocess moves to block 208, where it receives a current measurement froma sensor base or retrieves a current measurement from a database, andthen proceeds to block 210 where it computes a drift for each receivedsensor node measurement. In one embodiment, the drift can be defined asthe difference between the sensor node measurement and the sensor basemeasurement at or near a particular spatial location and a particulartime, as shown in FIG. 2B. As seen in the upper graph (a) of FIG. 2B,the sensor nodes have data drift while continuing their air sensing(sensing concentration drooping down as shown in the figure). Withoutthe data correction, the result from sensor node will provide the wrongmonitoring result by misleading reduction of pollutant concentration.But with the additional accurate data from the sensor base'shigh-performance analyzer (mobile or stationary), the drifted results ofthe sensor node are corrected based on the sensor base result measuredat substantially the same time and substantially the same location(although the sensor base data might not provide more discretemonitoring in time for each sensor node). Drifts can be computed formeasurements of total pollutant concentrations, for concentrations ofindividual compounds, or both. For instance, in a sensor node S that cansense five different pollutants, there can be a single drift for thetotal pollutant concentration, there can be five separate drifts, onefor each pollutant, or there can be both total and individual drifts.

Having computed a drift for each sensor node measurement at block 210,the process proceeds to block 212 where the sensor node measurements arecorrected. After the sensor node data correction using the sensor baseresult, the data drift issue of the low-cost sensor node can be moreaccurately obtained as shown in the lower graph (b) of FIG. 2B. Withsuch a method, hundreds or thousands of sensor node data can becorrected and achieve reliable sensing network results. The process thenproceeds to block 214, where the sensor node measurements are output toa user for further analysis or information. The process then returns toblock 204 where it can receive further measurements from sensor nodes.

Having computed a drift for each sensor node at block 210, in additionto moving to block 212 the process can also move to block 216, were itchecks whether the computed drift is greater than some threshold thatindicates that a sensor node must be replaced or recalibrated. If atblock 216 the drift of a particular sensor node is greater than some setthreshold, the process moves on to block 218, where it signals to a userthat replacement or recalibration of the sensor node is required. Afterblock 218, the process returns to block 204 to receive measurements fromthe same or other sensor nodes. But if at block 216 the drift of aparticular sensor node is less than some set threshold, the processreturns to block 204 to receive measurements from the same or othersensor nodes.

FIG. 3 illustrates an embodiment of positioning sensor bases and sensornodes in an air quality monitoring system 300. In system 300, the sensornodes S and sensor bases B are fixed in space relative to each other,are stationary, and are deployed at various spatial locations. Thecoverage area of each sensor base B encompasses one or more sensor nodesS, such that the coverage areas of each sensor base and itscorresponding sensor nodes overlap. Each sensor base can providereference data for corresponding sensor nodes in the sensor basecoverage area.

FIG. 4 illustrates another embodiment of positioning of sensor bases andsensor nodes in an air quality monitoring system 400. In system 400 thesensor nodes S are deployed at various spatial locations. A mobilesensor base 402 travels along a path 404 that brings it near enough toeach sensor node S to allow it to collect a sample at or near thelocation where each sensor node is installed and analyze the collectedsamples. Using a mobile sensor base B allows a small number of sensorbases to cover a larger number of sensor nodes S, so that spatiallydense reference data can be obtained for data correction of each sensornode.

FIG. 5 illustrates another embodiment of positioning sensor bases andsensor nodes in an air quality monitoring system 500. In system 500 aseries of stationary sensor bases B and various sensor nodes S aredeployed at various spatial locations. A mobile sensor base 502 travelsalong a path 504 that allows it to collect samples at or near thelocation where each sensor base B and each sensor node S is installedand analyze the collected samples. By doing so, mobile sensor base 502serves as a backup to the multiple fixed sensor bases B and alsocollects and provides reference data for data correction of each sensornode. In some extreme cases, some sensor nodes S may be installed atlocations where they are not covered by any fixed sensor bases B. Themobile sensor base can be used to provide the reference data for driftcorrection needed on those sensor nodes.

FIG. 6 illustrates another embodiment of positioning sensor bases andsensor nodes in an air quality monitoring system 600. FIG. 6 illustrateslow-cost sensor nodes S deployed in a city, primarily outside of abuilding, in the streets, and on the sidewalks, to form a sensingnetwork to monitor urban air quality. Mobile sensor bases 602, which canfor instance be sensor bases installed on vehicles for mobilemonitoring, can drive through city streets near known locations ofsensor nodes S to collect reference data when the sensor base moves tocorresponding nearby sensing nodes.

FIG. 7 illustrates another embodiment of positioning sensor bases andsensor nodes in an air quality monitoring system 700. FIG. 7 illustrateslow-cost sensor nodes S deployed inside a building 704 with ahigh-performance analyzer serving as sensor base B. Manifold tubes orpipes 702 are extended from sensor base B to different floors andlocations for air quality monitoring in the interior of the building.The sensor base data for each floor/location can be used to correct thesensor node data.

FIG. 8 illustrates another embodiment of positioning sensor bases andsensor nodes in an air quality monitoring system 800. Sensor nodes S areinstalled at various locations inside and outside buildings in a city(although the embodiment is not limited to a city). Stationary sensorbases B can be, but aren't necessarily, installed inside or outside thebuilding, with or without manifold sampling connected to air of interestfrom the stationary base station. Additional mobile sensor bases 802 canbe used to transport the high-performance air quality analyzers tocorresponding sensor node locations, which can cover sensor nodespositioned at locations the stationary sensor bases cannot reach formonitoring.

The above description of embodiments, including what is described in theabstract, is not intended to be exhaustive or to limit the invention tothe described forms. Specific embodiments of, and examples for, theinvention are described herein for illustrative purposes, but variousequivalent modifications are possible within the scope of the inventionin light of the above detailed description, as those skilled in therelevant art will recognize.

1. A sensing network comprising: one or more sensor nodes, wherein eachof the one or more sensor nodes includes a gas sensor that can measurethe presence, concentration, or both, of one or more airborne pollutantsin a nodal coverage area surrounding the sensor node; a sensor base thatcan measure the presence, concentration, or both, of one or moreairborne pollutants in the nodal coverage area of each of the one ormore sensor nodes, wherein the sensor base includes a gas sensor withhigher accuracy, higher sensitivity, or both, than the gas sensors ofthe one or more sensor nodes; and one or more servers communicativelycoupled to the sensor base and the one or more sensors nodes; whereinthe sensor base and the sensor nodes can communicate their measurementsto the server and wherein the measurements of the sensor base are usedby the server as a reference to correct the measurements of the one ormore sensor nodes.
 2. The sensing network of claim 1 wherein the sensorbase collects air samples at or around the nodal coverage area of eachsensor node.
 3. The sensing network of claim 2 wherein the sensor baseis fixed and has one or more sampling tubes through which it can collectsamples from the nodal coverage area of each sensor node.
 4. The sensingnetwork of claim 2 wherein the sensor base is fixed and can collect airsamples in a base coverage area surrounding the sensor base, and whereinthe base coverage area encompasses the nodal coverage areas of the oneor more sensor nodes so that a sample collected by the sensor base canbe considered representative of conditions in the nodal coverage areasof each the one or more sensor nodes.
 5. The sensing network of claim 2wherein the sensor base is mobile and can be moved into and out of thenodal coverage areas of each the one or more sensor nodes to collectsamples from the nodal coverage areas.
 6. The sensing network of claim 1wherein the measurements received from the sensor base are used torecalibrate the one or more sensor nodes.
 7. The sensing network ofclaim 1 wherein the sensor base and the one or more sensor nodes can becommunicatively coupled to the data center wirelessly, by wire, or both.8. The sensing network of claim 7 wherein the one or more sensor nodescan be communicatively coupled to the sensor base wirelessly, by wire,or both.
 9. The sensing network of claim 8 wherein the one or moresensor nodes can be communicatively coupled to the data center via thesensor base.
 10. The sensing network of claim 1 wherein the sensor basecollects air samples at or around the nodal coverage area of each sensornode at a specified fixed frequency or special temporal period.
 11. Thesensing network of claim 10 wherein the sensor base collects air samplesat or around the nodal coverage area of each sensor node at a frequencyor temporal duration that depends on the drift rate of the one or moresensor nodes.
 12. The sensing network of claim 1 wherein correcting themeasurements of the one or more sensing nodes comprises: computing adrift for each sensing node, the drift being computed based on thedifference between the measurement of a particular pollutant by eachsensor node and the measurement of the particular pollutant by thesensor base; and adjusting the measurements obtained for the particularpollutant from each sensor node based on the drift computed for theparticular pollutant at each sensing node.
 13. The sensing network ofclaim 1 wherein correcting the measurements of the one or more sensingnodes comprises: computing a drift for each sensing node, the driftbeing computed based on the difference between the measurement of atotal concentration of pollutants by each sensor node and themeasurement of the total concentration of pollutants by the sensor base;and adjusting the measurements obtained for the total concentration ofpollutants from each sensor node based on the drift computed for thetotal concentration of pollutants at each sensing node.
 14. The sensingnetwork of claim 1 wherein correcting the measurements of the one ormore sensing nodes comprises: computing a separate drift for eachpollutant sensed at each sensing node, each drift being computed basedon the difference between the measurement of a concentration of eachpollutant by each sensor node and the measurement of the concentrationof each pollutant by the sensor base; and adjusting the measurementsobtained for the concentration of each pollutant from each sensor nodebased on the drift computed for each pollutant.
 15. The sensing networkof claim 1 wherein the at least one pollutants are organic or inorganicchemicals, airborne particulate matter (PM), heavy metal contaminants,or airborne biological contaminants.
 16. A process comprising:transmitting a measurement from one or more sensor nodes to one or moreservers communicatively coupled to the one or more sensors nodes,wherein each of the one or more sensor nodes includes a gas sensor thatcan measure the presence, concentration, or both, of at least oneairborne chemical in a nodal coverage area surrounding the sensor node;transmitting a measurement from a sensor base to the one or moreservers, wherein the sensor base is communicatively coupled to the oneor more servers, wherein the sensor base can measure the presence,concentration, or both, of at least one airborne chemical in the nodalcoverage area of each of the one or more sensor nodes, and wherein thesensor base includes a gas sensor with higher accuracy, highersensitivity, or both, than the gas sensors in the one or more sensornodes; and correcting the measurements from the one or more sensor nodesusing the measurements received from the sensor base.
 17. The process ofclaim 16 wherein the sensor base collects air samples at or around thenodal coverage area of each sensor node.
 18. The process of claim 17wherein the sensor base is fixed and has one or more sampling tubesthrough which it can collect samples from the nodal coverage area ofeach sensor node.
 19. The process of claim 17 wherein the sensor base isfixed and can collect air samples in a base coverage area surroundingthe sensor base, and wherein the base coverage area encompasses thenodal coverage areas of the one or more sensor nodes so that a samplecollected by the sensor base can be considered representative ofconditions in the nodal coverage areas of each the one or more sensornodes.
 20. The process of claim 17 wherein the sensor base is mobile andcan be moved into and out of the nodal coverage areas of each the one ormore sensor nodes to collect samples from the nodal coverage areas. 21.The process of claim 16 wherein the measurements received from thesensor base are used to recalibrate the one or more sensor nodes. 22.The process of claim 16 wherein the sensor base and the one or moresensor nodes can be communicatively coupled to the data centerwirelessly, by wire, or both.
 23. The process of claim 22 wherein theone or more sensor nodes can be communicatively coupled to the sensorbase wirelessly, by wire, or both.
 24. The process of claim 23 whereinthe one or more sensor nodes can be communicatively coupled to the datacenter via the sensor base.
 25. The process of claim 16 wherein the basenode collects air samples at or around the nodal coverage area of eachsensor node at a specified fixed frequency.
 26. The process of claim 25wherein the sensor base collects air samples at or around the nodalcoverage area of each sensor node at a frequency or specific temporalperiod that depends on the drift rate of the one or more sensor nodes.27. The process of claim 16 wherein correcting the measurements of theone or more sensing nodes comprises: computing a drift for each sensingnode, the drift being computed based on the difference between themeasurement of a particular pollutant by each sensor node and themeasurement of the particular pollutant by the sensor base; andadjusting the measurements obtained for the particular pollutant fromeach sensor node based on the drift computed for the particularpollutant at each sensing node.
 28. The process of claim 16 whereincorrecting the measurements of the one or more sensing nodes comprises:computing a drift for each sensing node, the drift being computed basedon the difference between the measurement of a total concentration ofpollutants by each sensor node and the measurement of the totalconcentration of pollutants by the sensor base; and adjusting themeasurements obtained for the total concentration of pollutants fromeach sensor node based on the drift computed for the total concentrationof pollutants at each sensing node.
 29. The process of claim 16 whereincorrecting the measurements of the one or more sensing nodes comprises:computing a separate drift for each pollutant sensed at each sensingnode, each drift being computed based on the difference between themeasurement of a concentration of each pollutant by each sensor node andthe measurement of the concentration of each pollutant by the sensorbase; and adjusting the measurements obtained for the concentration ofeach pollutant from each sensor node based on the drift computed foreach pollutant.
 30. The process of claim 16 wherein the at least onepollutants are organic or inorganic chemicals, airborne particulatematter (PM), heavy metal contaminants, or airborne biologicalcontaminants.